Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning by Tiantian Yu, Guiying Wang, Xia Xu, Jianying Yan

“…We identified 10 DECRGs conducted Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein–protein interaction (PPI) network. Furthermore, patients with PE were categorized into two distinct clusters, and an investigation was conducted to examine the status of immune cell infiltration. …”
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Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers by Hengyan Zhang, Ye Zhou, Heguo Yan, Changxing Huang, Licong Yang, Yangwen Liu

“…Meanwhile, protein-protein interaction (PPI) networks were constructed using STRING to identify core genes. …”
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Analysis of the lncRNA-Associated Competing Endogenous RNA (ceRNA) Network for Tendinopathy by Bing-Zhe Huang, Yang Jing-Jing, Xiao-Ming Dong, null Zhuan Zhong, null Xiao-Ning Liu

“…Differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) were identified, and functional and pathway enrichment analyses were performed. Protein-protein interaction (PPI) network was constructed and further analyzed by module mining. …”
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Transcriptomic Response of Arabidopsis thaliana to Pseudomonas syringae Infection: An In Silico Approach by Amir Ghaffar Shahriari, Mohamad Hamed Ghodoum Parizipour, Yaser Biniaz, Aminallah Tahmasebi, Fatemeh Gholizadeh

“…Also, critical factors, including hub genes, protein-protein interaction (PPI), and micro RNAs (miRNAs), were analyzed. …”
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Clinical Value and Potential Mechanism of miRNA-33a-5p in Lung Squamous Cell Carcinoma by Xiang-Ming Wang, Shang-Wei Chen, Gang Chen, Hua-Fu Zhou, Ting-Qing Gan, Jing-Jing Zeng, Zu-Yun Li

“…The differentially expressed genes (DEGs) of LUSC and the candidate target genes of miRNA-33a-5p were overlapped to acquire a set of specific genes for further analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and protein–protein interaction (PPI) network. miRNA-33a-5p overexpressed in LUSC was supported by 706 LUSC and 261 non-LUSC samples gathering from RT-qPCR, miRNA-seq, and public miRNA microarrays. …”
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Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning by Jianhui Chen, Qun Li, Xiaofang Liu, Fang Lin, Yaling Jing, Jiayan Yang, Lianfang Zhao

“…This study aims to investigate the potential biomarkers and immune infiltration in EMs and RPL, providing a basis for early detection and treatment of the two diseases.MethodsTwo RPL and six EMs transcriptomic datasets from the Gene Expression Omnibus (GEO) database were used for differential analysis via limma package, followed by weighted gene co-expression network analysis (WGCNA) for key modules screening. Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. …”
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Deciphering the multifaceted mechanism of Shuang Huang Lian in treating upper respiratory tract infections: A metabolomics-based network pharmacology approach by Gang Xu, Akshay Suresh Patil, Ruhan Wei, Dan Liu, Aimin Zhou, Yan Xu

“…Key cheminformatics and bioinformatics platforms and databases, including ADMETlab, SEA, PASS, Super-Pred, SwissTargetPrediction, PharmMapper, and GeneCards, were used to predict the drug-like properties of active SHL components and identify protein targets shared between the active SHL components and the URTI-related disease genes. A protein-protein interaction (PPI) network was constructed, and gene ontology (GO), KEGG, and Reactome enrichment analyses were conducted to elucidate the biological functions relevant to URTIs. …”
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Differential GTP-dependent in-vitro polymerization of recombinant Physcomitrella FtsZ proteins by Stella W. L. Milferstaedt, Marie Joest, Lennard L. Bohlender, Sebastian N. W. Hoernstein, Buğra Özdemir, Eva L. Decker, Chris van der Does, Ralf Reski

“…Here, we investigated the interaction of Physcomitrella FtsZ2-1 with FtsZ1 isoforms via co-immunoprecipitation and mass spectrometry, and found protein-protein interaction in vivo. We tagged FtsZ1-2 and FtsZ2-1 with different fluorophores and expressed both in E. coli, which led to the formation of defined structures within the cells and to an influence on bacterial cell division and morphology. …”
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The Underlying Molecular Basis and Mechanisms of Venous Thrombosis in Patients with Osteomyelitis: A Data-Driven Analysis by Peisheng Chen, Yinhuan Liu, Xiaofeng Lin, Bin Yu, Bin Chen, Fengfei Lin

“…The TRRUST database was used to identify the upstream transcription factors (TFs) that regulate VTEGs2 and OMGs2. The protein-protein interaction (PPI) network between VTEGs2 and OMGs2 was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and then visualized in Cytoscape. …”
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Genetic Determinants of Immune Cells and Hepatocellular Carcinoma Risk: A Bioinformatics and Bidirectional Mendelian Randomization Study by Tong WU, Fei GAO, Fei TENG, Qiaoli ZHANG

“…ResultsA total of 284 HCC-related genes were identified, with 120 genes recognized within the protein interaction network. Immune infiltration analysis revealed significant correlations between key genes and immune cells. …”
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Decoding oral cancer: insights from miRNA expression profiles and their regulatory targets by Xin Wang, Xin Wang, Shuang Zhang, Shuang Zhang, Shuyi Wang, Tao Cao, Tao Cao, Hong Fan, Hong Fan

“…Furthermore, different signaling cascades such as IGF signaling, PDGF signaling and LKB1 signaling and PLK1 signaling pathways were also found associated with DEmiR-related regulation of OC progression. Protein-protein interaction (PPI) network analysis identified key molecular hubs associated with DEmiR and DEGs in OC. …”
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Proteome‐Wide Mendelian Randomization Identifies Therapeutic Targets for Abdominal Aortic Aneurysm by Ka Zhang, Yuan Liu, Aiqin Mao, Changzhu Li, Li Geng, Hao Kan

“…Additionally, single‐cell expression analysis, protein–protein interaction network analysis, pathway enrichment analysis, and druggability assessments were conducted to identify cell types with enriched expression and prioritize potential therapeutic targets. …”
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Identification and functional analysis of energy metabolism and pyroptosis-related genes in diabetic nephropathy by Shan He, Jian Ye, Yu Wang, Lu yang Xie, Si Yi Liu, Qin kai Chen

“…Subsequent analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) network analysis. Additionally, mRNA-miRNA, mRNA-drug, and mRNA-transcription factor (TF) interaction networks were constructed. …”
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AN EXPERIMENTAL STUDY OF THE EFFECT OF RARE POLYMORPHISMS OF HUMAN HBB, HBD AND F9 PROMOTER TATA BOXES ON THE KINETICS OF INTERACTION WITH THE TATA-BINDING PROTEIN by E. B. Sharypova, I. A. Drachkova, E. V. Kashina, D. A. Rasskazov, P. M. Ponomarenko, M. P. Ponomarenko, N. А. Kolchanov, L. K. Savinkova

“…In the present work, we investigate the effect of rare not annotated single-nucleotide polymorphisms (SNPs) of TATA boxes of these genes with an unknown manifestation on the TATA-binding protein interaction. To study the kinetic characteristics of TBP/TATA complex formation in vitro, doublestranded oligodeoxynucleotides identical to the TATA-containing portions of the promoters of the HBB, HBD and F9 genes (“normal” and minor alleles) and recombinant human TBP were used. …”
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Glucometabolic-Related Genes as Diagnostic Biomarkers and Therapeutic Targets for Alzheimer’s Disease and Type 2 Diabetes Mellitus: A Bioinformatics Analysis by Shuo Liu, He Chen, Xiao-Dong He, Xiao-Ou Yang

“…A range of bioinformatics tools were employed for functional annotation, pathway enrichment, protein interaction network construction, module analysis, ROC curve assessment, correlation matrix construction, gene set enrichment analysis, and gene-drug interaction mapping of these DEGs. …”
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GPX3 as a Novel and Potential Therapeutic Target in the Shared Molecular Mechanisms of Traumatic Brain Injury and Parkinson’s Disease by Wang Y, Fang J, Yuan Q, Yu J, Hu J

“…Further analyses, including GO and KEGG pathway enrichment, protein-protein interaction (PPI) network construction, hub gene identification, as well as miRNA and transcription factor prediction and drug candidate screening, were conducted. …”
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Molecular Characterization and Clinical Characteristics of m5C-Based RNA Methylation in Spinal Cord Injury: Validated by qPCR by Liang Cao, Wen Jun Pi, Qiang Zhang, Qing Li

“…Through the creation of a protein–protein interaction network, we screened 11 hub genes. We demonstrated their importance between SCI group and sham group using real-time reverse transcription-quantitative polymerase chain reaction in rat model. …”
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A Network Pharmacology-Based Approach for Exploring Key Active Compounds and Pharmacological Mechanisms of Tangshen Formula for Treatment of Diabetic Nephropathy by Weie Zhou, Xuefeng Zhou, Yuan Zhang, Yuyang Wang, Wenjie Wu, Haojun Zhang, Tingting Zhao, Liang Peng, Hailing Zhao, Ping Li

“…A network pharmacology approach was firstly applied to explore multiple active compounds and multiple key pharmacological mechanisms for TSF treating DN by drug-targeted interaction databases, herb-compound-target network, protein-protein interaction network, compound-target-pathway network, and analysis methods. …”
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Identification and functional analysis of hub genes involved in deoxynivalenol-induced enterotoxicity in porcine (Sus scrofa) by Jinhua He, Geng Zhao, Mingxia Chen, Ximing Ren, Peizhi Zhu, Zhizhong Liu, Jiayi Zhou, Hanwei Chen, Chuqiao Xiao, Xiang-Guang Li

“…Then, genes in the highest co-expressed module were intersected with the up-regulated DEGs to construct a Protein-Protein Interaction network, resulting in 237 overlapping genes. …”
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Exploring the multi-targeted mechanism of Saikosaponin A in prostate cancer treatment: a network pharmacology and molecular docking approach by Genbao Zhu, Genbao Zhu, Genbao Zhu, Zhiming Jiang, Niuping Zhu, Niuping Zhu, Donghui Wang, Donghui Wang, Tianpeng Guo, Tianpeng Guo, Yiqing Meng, Yiqing Meng, Yi Zhu, Yi Zhu, Kemeng Tan, Mengxue Hu, Heng Tang, Xuannian Wang, Xuannian Wang

“…However, the mechanism underlying the effects of SSA in prostate cancer treatment remains elusive.MethodsIn this study, a network pharmacology approach was applied to identify relevant targets from drug- and disease-related databases, and intersections were analyzed using Venny2.1 to construct a Protein-Protein interaction (PPI) interaction network. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to elucidate associated biological functions and signaling pathways. …”
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